Cited by 272Speech Recognition and SynthesisSpeech and Audio ProcessingVoice and Speech Disorders

The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.

CycleGAN-VC2: improved CycleGAN-based non-parallel voice conversion.

Kaneko, Takuhiro, Kameoka, Hirokazu, Tanaka, Kou, Hojo, Nobukatsu·2019·ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

DOI 10.1109/icassp.2019.8682897

Open this paper in CoChat — free account

Abstract

Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data. This is an important task, but it has been challenging due to the disadvantages of the training conditions. Recently, CycleGAN-VC has provided a breakthrough and performed comparably to a parallel VC method without relying on any extra data, modules, or time alignment procedures. However, there is still a large gap between the real target and converted speech, and bridging this gap remains a challenge. To reduce the gap, we propose CycleGAN-VC2, which is an improved version of CycleGAN-VC incorporating three new techniques: an improved objective (two-step adversarial losses), improved generator (2-1-2D CNN), and improved discriminator (PatchGAN). We evaluated our method on a non-parallel VC task and analyzed the effect of each technique in detail. An objective evaluation showed that these techniques help bring the converted feature sequence closer to the target in terms of both global and local structures, which we assess by using Mel-cepstral distortion and modulation spectra distance, respectively. A subjective evaluation showed that CycleGAN-VC2 outperforms CycleGAN-VC in terms of naturalness and similarity for every speaker pair, including intra-gender and inter-gender pairs.

Integrity signals

Registry

Crossref · 10.1109/icassp.2019.8682897

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911

Show all 58 references

Sorted by citations

Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward
Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543
An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning
Şişman, B., Yamagishi, J., King, S., & Li, H. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3038524 · Cited by 399
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2236 · Cited by 170
Self-citation
Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review
Khan, W., Zaki, N., & Ali, L. (2021) · IEEE Access · doi:10.1109/access.2021.3069937 · Cited by 133
A Review of Synthetic Image Data and Its Use in Computer Vision
Man, K., & Chahl, J. (2022) · Journal of Imaging · doi:10.3390/jimaging8110310 · Cited by 132
Fetal ECG Extraction From Maternal ECG Using Attention-Based CycleGAN
Mohebbian, M. R., Vedaei, S. S., Wahid, K. A., Dinh, A., Marateb, H. R., & Tavakolian, K. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3111873 · Cited by 109
Data Augmentation for Deep Neural Networks Model in EEG Classification Task: A Review
He, C., Liu, J., Zhu, Y., & Du, W. (2021) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2021.765525 · Cited by 105
Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations
Zhang, J.-X., Ling, Z.-H., & Dai, L.-R. (2019) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2019.2960721 · Cited by 105
Again-VC: A One-Shot Voice Conversion Using Activation Guidance and Adaptive Instance Normalization
Chen, Y.-H., Wu, D.-Y., Wu, T.-H., & Lee, H.-Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414257 · Cited by 107
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-Spectrogram Conversion
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-2280 · Cited by 96
Self-citation
CycleGAN for undamaged-to-damaged domain translation for structural health monitoring and damage detection
Luleci, F., Çatbaş, F. N., & Avcı, O. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.110370 · Cited by 95
Understanding GANs: fundamentals, variants, training challenges, applications, and open problems
Ahmad, Z., Jaffri, Z. U. A., Chen, M., & Bao, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19361-y · Cited by 96
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Triantafyllopoulos, A., Schuller, B. W., İymen, G., Sezgin, T. M., He, X., Yang, Z., Tzirakis, P., Liu, S., Mertes, S., André, E., et al. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3250266 · Cited by 102
Nonparallel Emotional Speech Conversion
Gao, J., Chakraborty, D., Tembiné, H., & Olaleye, O. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-2878 · Cited by 75
Maskcyclegan-VC: Learning Non-Parallel Voice Conversion with Filling in Frames
Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414851 · Cited by 76
Self-citation
Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data
Zhou, K., Şişman, B., & Li, H. (2020) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2020-33 · Cited by 67
TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection
Salvi, D., Hosler, B., Bestagini, P., Stamm, M. C., & Tubaro, S. (2023) · IEEE Access · doi:10.1109/access.2023.3276480 · Cited by 65
Defending Your Voice: Adversarial Attack on Voice Conversion
Huang, C., Lin, Y. Y., Lee, H.-Y., & Lee, L.-S. (2021) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt48900.2021.9383529 · Cited by 57
A novel deep learning technique for morphology preserved fetal ECG extraction from mother ECG using 1D-CycleGAN
Basak, P., Sakib, A. H., Chowdhury, M. E. H., Al-Emadi, N. A., Yalcin, H. C., Pedersen, S., Mahmud, S., Serkan Kiranyaz, B., & Al-Maadeed, S. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121196 · Cited by 55
A Parallel-Data-Free Speech Enhancement Method Using Multi-Objective Learning Cycle-Consistent Generative Adversarial Network
Xiang, Y., & Bao, C. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2997118 · Cited by 54
Low-Resource Expressive Text-To-Speech Using Data Augmentation
Huybrechts, G., Merritt, T., Comini, G., Perz, B., Shah, R., & Lorenzo-Trueba, J. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413466 · Cited by 50
Unsupervised Representation Disentanglement Using Cross Domain Features and Adversarial Learning in Variational Autoencoder Based Voice Conversion
Huang, W.-C., Luo, H., Hwang, H.-T., Lo, C.-C., Peng, Y.-H., Tsao, Y., & Wang, H.-M. (2020) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2020.2977678 · Cited by 48
SINGAN: Singing Voice Conversion with Generative Adversarial Networks
Şişman, B., Vijayan, K., Dong, M., & Li, H. (2019) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc47483.2019.9023162 · Cited by 46
T5-Based Model for Abstractive Summarization: A Semi-Supervised Learning Approach with Consistency Loss Functions
Wang, M., Xie, P., Du, Y., & Hu, X. (2023) · Applied Sciences · doi:10.3390/app13127111 · Cited by 46
Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition
Jin, Z., Geng, M., Deng, J., Wang, T., Hu, S., Li, G., & Liu, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3323888 · Cited by 52
Voice In Ear
Gao, Y., Jin, Y., Chauhan, J., Choi, S., Li, J., & Jin, Z. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448113 · Cited by 45
Detecting Voice Cloning Attacks via Timbre Watermarking
Liu, C., Zhang, J., Zhang, T., Yang, X., Zhang, W., & Yu, N. (2024) · Proceedings 2024 Network and Distributed System Security Symposium · doi:10.14722/ndss.2024.24200 · Cited by 40
Many-To-Many Voice Conversion Using Conditional Cycle-Consistent Adversarial Networks
Lee, S., Ko, B., Lee, K., Yoo, I.-C., & Yook, D. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053726 · Cited by 38
Seismic Data Augmentation Based on Conditional Generative Adversarial Networks
Li, Y., Ku, B., Zhang, S., Ahn, J.-K., & Ko, H. (2020) · Sensors · doi:10.3390/s20236850 · Cited by 39
Generative Adversarial Networks in Human Emotion Synthesis: A Review
Hajarolasvadi, N., Ramírez, M. A., Beccaro, W., & Demirel, H. (2020) · IEEE Access · doi:10.1109/access.2020.3042328 · Cited by 37
MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using SpectrogramsPreprint
Pasini, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03713 · Cited by 38
An Adaptive-Learning-Based Generative Adversarial Network for One-to-One Voice Conversion
Dhar, S., Jana, N. D., & Das, S. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3149858 · Cited by 28
Nonparallel Voice Conversion With Augmented Classifier Star Generative Adversarial Networks
Kameoka, H., Kaneko, T., Tanaka, K., & Hojo, N. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3036784 · Cited by 28
Self-citation
Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants
Aloufi, R., Haddadi, H., & Boyle, D. (2019) · arXiv · Cited by 39
Duration Controllable Voice Conversion via Phoneme-Based Information Bottleneck
Lee, S.-H., Noh, H.-R., Nam, W.-J., & Lee, S. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3156757 · Cited by 25
Sequence-to-Sequence Emotional Voice Conversion With Strength Control
Choi, H., & Hahn, M. (2021) · IEEE Access · doi:10.1109/access.2021.3065460 · Cited by 23
An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep LearningPreprint
Şişman, B., Yamagishi, J., King, S., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.03648 · Cited by 23
Voice Conversion with Transformer Network
Liu, R., Chen, X., & Wen, X. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054523 · Cited by 22
High-Intelligibility Speech Synthesis for Dysarthric Speakers with LPCNet-Based TTS and CycleVAE-Based VC
Matsubara, K., Okamoto, T., Takashima, R., Takiguchi, T., Toda, T., Shiga, Y., & Kawai, H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414136 · Cited by 21
VoiceGrad: Non-Parallel Any-to-Many Voice Conversion With Annealed Langevin Dynamics
Kameoka, H., Kaneko, T., Tanaka, K., Hojo, N., & Seki, S. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3379901 · Cited by 24
Self-citation
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
Tang, H., Zhang, X., Wang, J., Cheng, N., Zeng, Z., Xiao, E., & Xiao, J. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9688088 · Cited by 19
Dance Style Transfer with Cross-modal Transformer
Yin, W., Yin, H., Baraka, K., Kragić, D., & Björkman, M. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00503 · Cited by 19
E-DGAN: An Encoder-Decoder Generative Adversarial Network Based Method for Pathological to Normal Voice Conversion
Chu, M., Yang, M., Xu, C., Ma, Y., Wang, J., Fan, Z., Tao, Z., & Wu, D. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3239551 · Cited by 19
STATNet: Spectral and Temporal features based Multi-Task Network for Audio Spoofing Detection
Ranjan, R., Vatsa, M., & Singh, R. (2022) · 2022 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007949 · Cited by 18
Expressive Voice Conversion: A Joint Framework for Speaker Identity and Emotional Style Transfer
Du, Z., Şişman, B., Zhou, K., & Li, H. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687906 · Cited by 18
Multimodal dance style transfer
Yin, W., Yin, H., Baraka, K., Kragić, D., & Björkman, M. (2023) · Machine Vision and Applications · doi:10.1007/s00138-023-01399-x · Cited by 18
Improved Relativistic Cycle-Consistent GAN With Dilated Residual Network and Multi-Attention for Speech Enhancement
Wang, Y., Yu, G., Wang, J., Wang, H., & Zhang, Q. (2020) · IEEE Access · doi:10.1109/access.2020.3029417 · Cited by 18
MS-GAN: multi-scale GAN with parallel class activation maps for image reconstruction
Rao, J., Ke, A., Liu, G., & Ming, Y. (2022) · The Visual Computer · doi:10.1007/s00371-022-02468-4 · Cited by 18
Data Augmentation Using CycleGAN for End-to-End Children ASR
Singh, D. K., Amin, P. P., Sailor, H. B., & Patil, H. A. (2021) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco54536.2021.9616228 · Cited by 17
Navigating the Soundscape of Deception: A Comprehensive Survey on Audio Deepfake Generation, Detection, and Future Horizons
Wani, T. M., Qadri, S. A. A., Wani, F. A., & Amerini, I. (2024) · Foundations and Trends® in Privacy and Security · doi:10.1561/3300000048 · Cited by 16
Crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
Kobayashi, K., Huang, W.-C., Wu, Y.-C., Tobing, P. L., Hayashi, T., & Toda, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413959 · Cited by 16
Emotion Filtering at the Edge
Aloufi, R., Haddadi, H., & Boyle, D. (2019) · doi:10.1145/3362743.3362960 · Cited by 16
Face-based Voice Conversion: Learning the Voice behind a Face
Lu, H.-H., Weng, S.-E., Yen, Y.-F., Shuai, H.-H., & Cheng, W.-H. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475198 · Cited by 15
Identifying Source Speakers for Voice Conversion Based Spoofing Attacks on Speaker Verification Systems
Cai, D., Cai, Z., & Li, M. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096733 · Cited by 16
VoicePM: A Robust Privacy Measurement on Voice Anonymity
Zhang, S., Li, Z., & Das, A. (2023) · ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec) · doi:10.1145/3558482.3590175 · Cited by 15
Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training DataPreprint
Zhou, K., Şişman, B., & Li, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00198 · Cited by 15
Cyclegean: Cycle Generative Enhanced Adversarial Network for Voice Conversion
Zhang, X., Wang, J., Cheng, N., Xiao, E., & Xiao, J. Z. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687948 · Cited by 14
Many-to-Many Voice Conversion Using Cycle-Consistent Variational Autoencoder with Multiple Decoders
Yook, D., Leem, S.-G., Lee, K., & Yoo, I.-C. (2020) · The Speaker and Language Recognition Workshop (Odyssey) · doi:10.21437/odyssey.2020-31 · Cited by 14
Learning the Beauty in Songs: Neural Singing Voice Beautifier
Liu, J., Li, C., Ren, Y., Zhu, Z., & Zhao, Z. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.549 · Cited by 14
Style Equalization: Unsupervised Learning of Controllable Generative Sequence ModelsPreprint
Chang, J.-H. R., Shrivastava, A., Koppula, H. S., Zhang, X., & Tuzel, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02891 · Cited by 15
PAVITS: Exploring Prosody-Aware VITS for End-to-End Emotional Voice Conversion
Qi, T., Zheng, W., Lu, C., Zong, Y., & Lian, H. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446191 · Cited by 13
CycleGAN-VC-GP: Improved CycleGAN-based Non-parallel Voice Conversion
Wang, C., & Yu, Y. (2020) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct50939.2020.9295938 · Cited by 13
Zero-Shot Voice Conversion with Adjusted Speaker Embeddings and Simple Acoustic Features
Tan, Z., Wei, J., Xu, J., He, Y., & Lu, W. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414975 · Cited by 13
Region Normalized Capsule Network Based Generative Adversarial Network for Non-parallel Voice Conversion
Akhter, M. T., Banerjee, P., Dhar, S., Ghosh, S., & Jana, N. D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-48309-7_20 · Cited by 12
GLGAN-VC: A Guided Loss-Based Generative Adversarial Network for Many-to-Many Voice Conversion
Dhar, S., Jana, N. D., & Das, S. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3335119 · Cited by 12
Towards Low-Resource Stargan Voice Conversion Using Weight Adaptive Instance Normalization
Chen, M., Shi, Y., & Hain, T. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9415042 · Cited by 12
Voice privacy using CycleGAN and time-scale modification
Prajapati, G. P., Singh, D. K., Amin, P. P., & Patil, H. A. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101353 · Cited by 12
The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection
Mari, D., Latora, F., & Milani, S. (2022) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs55849.2022.9975404 · Cited by 12
CVC: Contrastive Learning for Non-Parallel Voice Conversion
Li, T., Liu, Y., Hu, C., & Zhao, H. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-137 · Cited by 12
End-to-End Zero-Shot Voice Conversion with Location-Variable Convolutions
Kang, W., Hasegawa‐Johnson, M., & Roy, D. K. (2023) · Interspeech (USB) · doi:10.21437/interspeech.2023-2298 · Cited by 13
Conan's Bow Tie: A Streaming Voice Conversion for Real-Time VTuber Livestreaming
Chen, Q., Gu, Z. P., Lü, L., Xu, X., Ba, Z., Lin, F., Liu, Z., & Ren, K. (2024) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3640543.3645146 · Cited by 11
Voice Conversion with Denoising Diffusion Probabilistic GAN Models
Zhang, X., Wang, J., Cheng, N., & Xiao, J. Z. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46674-8_11 · Cited by 11
A Modularized Neural Network with Language-Specific Output Layers for Cross-Lingual Voice Conversion
Zhou, Y., Tian, X., Yılmaz, E., Das, R. K., & Li, H. (2019) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru46091.2019.9003798 · Cited by 11
CycleGAN-Based Visible-Infrared Image Enhancement Method for Infrared Power Equipment Object Detection
Yin, J., Li, Z., Cui, L., Zhang, W., Wang, Q., & Si, G. (2023) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics58376.2023.10235612 · Cited by 10
Any-to-Any Voice Conversion with F0 and Timbre Disentanglement and Novel Timbre Conditioning
Kovela, S., Valle, R., Dantrey, A., & Catanzaro, B. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096220 · Cited by 10
RefXVC: Cross-Lingual Voice Conversion With Enhanced Reference Leveraging
Zhang, M., Zhou, Y., Ren, Y., Zhang, C., Yin, X., & Li, H. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3439996 · Cited by 9
CycleGAN Voice Conversion of Spectral Envelopes using Adversarial Weights
Ferro, R., Obin, N., & Röebel, A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287643 · Cited by 10
Emotion Speech Synthesis Method Based on Multi-Channel Time–Frequency Domain Generative Adversarial Networks (MC-TFD GANs) and Mixup
Jia, N., & Zheng, C. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-06090-9 · Cited by 9
DGC-Vector: A New Speaker Embedding for Zero-Shot Voice Conversion
Xiao, R., Zhang, H., & Lin, Y. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746278 · Cited by 9
HiFi-SVC: Fast High Fidelity Cross-Domain Singing Voice Conversion
Zhou, Y., & Lu, X. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746812 · Cited by 9
Unsupervised Audiovisual Synthesis via Exemplar AutoencodersPreprint
Deng, K., Bansal, A., & Ramanan, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04463 · Cited by 12
CASIA Voice Conversion System for the Voice Conversion Challenge 2020
Lian, Z., Tao, J., Wen, Z., & Zhong, R. (2020) · doi:10.21437/vcc_bc.2020-19 · Cited by 8
Feature Map Regularized CycleGAN for Domain Transfer
Krstanović, L., Popovic, B. Z., Janev, M. B., & Brkljač, B. (2023) · Mathematics · doi:10.3390/math11020372 · Cited by 8
MediumVC: Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium featuresPreprint
Gu, Y., Zhang, Z., Yi, X., & Zhao, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.02500 · Cited by 8
Transfer Learning From Speech Synthesis to Voice Conversion With Non-Parallel Training Data
Zhang, M., Zhou, Y., Li, Z., & Li, H. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3066047 · Cited by 7
CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
Yook, D., Han, G., Chang, H.-P., & Yoo, I.-C. (2024) · Applied Sciences · doi:10.3390/app14209595 · Cited by 7
Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples
Sun, A., Wang, J., Cheng, N., Tantrawenith, M., Wu, Z., Meng, H. M. L., Xiao, E., & Xiao, J. Z. (2021) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru51503.2021.9687965 · Cited by 7
Voice Conversion Using Feature Specific Loss Function Based Self-Attentive Generative Adversarial Network
Dhar, S., Banerjee, P., Jana, N. D., & Das, S. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095069 · Cited by 7
A Style Transfer Approach to Source Separation
Venkataramani, S., Tzinis, E., & Smaragdis, P. (2019) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa.2019.8937203 · Cited by 7
Random Cycle Loss and Its Application to Voice Conversion
Sun, H., Wang, D., Li, L., Chen, C., & Zheng, T. F. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3257839 · Cited by 7
Non-Parallel Voice Conversion Using Cycle-Consistent Adversarial Networks with Self-Supervised Representations
Chun, C., Lee, Y. H., Lee, G. W., Jeon, M., & Kim, H. K. (2023) · IEEE Consumer Communications and Networking Conference · doi:10.1109/ccnc51644.2023.10060510 · Cited by 7
MFCCGAN: A Novel MFCC-Based Speech Synthesizer Using Adversarial Learning
Hasanabadi, M. R., Behdad, M., & Gharavian, D. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095873 · Cited by 7
One-Shot Voice Conversion Based on Speaker Aware Module
Zhang, Y., Che, H., Li, J., Li, C., Wang, X., & Wang, Z. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414081 · Cited by 7
Attention-based Multimodal learning framework for Generalized Audio-Visual Deepfake Detection
Masood, M., Javed, A., & Irtaza, A. (2023) · Preprint (Research Square) · doi:10.21203/rs.3.rs-3415144/v1 · Cited by 6
Reimagining speech: a scoping review of deep learning-based methods for non-parallel voice conversion
Bargum, A. R., Serafin, S., & Erkut, C. (2024) · Frontiers in Signal Processing · doi:10.3389/frsip.2024.1339159 · Cited by 13
CycleGANAS: Differentiable Neural Architecture Search for CycleGAN
An, T., & Joo, C. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00172 · Cited by 6
MADD: A Multi-Lingual Multi-Speaker Audio Deepfake Detection Dataset
Qi, X., Gu, H., Yi, J., Tao, J., Ren, Y., He, J., & Zeng, S. (2024) · International Symposium on Chinese Spoken Language Processing (ISCSLP) · doi:10.1109/iscslp63861.2024.10800535 · Cited by 7
Singing Voice Conversion Based on WD-GAN Algorithm
Zhao, W., Wang, W., Sun, Y., & Tang, T. (2019) · IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) · doi:10.1109/iaeac47372.2019.8997824 · Cited by 6
Effectiveness of Transfer Learning on Singing Voice Conversion in the Presence of Background Music
Rajpura, D. G., Shah, J., Patel, M., Malaviya, H., Phatnani, K. S., & Patil, H. A. (2020) · International Conference on Signal Processing and Communications (SPCOM) · doi:10.1109/spcom50965.2020.9179583 · Cited by 6
FID-RPRGAN-VC: Fréchet Inception Distance Loss based Region-wise Position Normalized Relativistic GAN for Non-Parallel Voice Conversion
Dhar, S., Akhter, M. T., Banerjee, P., Jana, N. D., & Das, S. (2023) · Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) · doi:10.1109/apsipaasc58517.2023.10317438 · Cited by 6

Authors: Kaneko, T., Kameoka, H., Tanaka, K., & Hojo, N.
Affiliations: NTT (Japan) (JP)
ORCID: Kaneko, Takuhiro
Kameoka, Hirokazu
Tanaka, Kou
Hojo, Nobukatsu
Venue: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publisher: IEEE
Published: 2019-05
Pages: 6820-6824
ISSN: 1520-6149 (ISSN-L)
DOI: 10.1109/icassp.2019.8682897
OpenAlex: W2937579788
Cited by: 272
References: 58
Citations by year: 2026: 14 · 2025: 34 · 2024: 36 · 2023: 57 · 2022: 39 · 2021: 43 · 2020: 34 · 2019: 15
FWCI: 12.53
Type: conference paper
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE International Conference on Acoustics Speech and Signal Processing (publishedVersion)
Funders: Virginia Agricultural Experiment Station, Virginia Polytechnic Institute and State University
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
Keywords: non-parallel voice conversion, adversarial training, PatchGAN discriminator, Mel cepstral distortion, speech naturalness

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

CycleGAN-VC2: improved CycleGAN-based non-parallel voice conversion.

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks